This paper presents the first interpretation attack on electronic health records (EHRs). Our research shows that our attack can attain significant success on EHR interpretations that do not rely on model gradients. We introduce metrics compatible with EHR data to evaluate the attack's success. Moreover, our findings demonstrate that detection methods that have successfully identified conventional adversarial examples are ineffective against our attack. This paper also proposes a defense method utilizing auto-encoders to de-noise the data and improve the interpretations' robustness.